A technique for computer detection and correction of spelling errors
Communications of the ACM
Exploring distributional similarity based models for query spelling correction
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
A Contextual Postprocessing System for Error Correction Using Binary n-Grams
IEEE Transactions on Computers
Joint-sequence models for grapheme-to-phoneme conversion
Speech Communication
Online expansion of rare queries for sponsored search
Proceedings of the 18th international conference on World wide web
Extending autocompletion to tolerate errors
Proceedings of the 2009 ACM SIGMOD International Conference on Management of data
Multi-style language model for web scale information retrieval
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
Learning phrase-based spelling error models from clickthrough data
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
A large scale ranker-based system for search query spelling correction
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
Scalable, flexible and generic instant overview search
Proceedings of the 21st international conference companion on World Wide Web
A generalized hidden Markov model with discriminative training for query spelling correction
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
A unified approach to transliteration-based text input with online spelling correction
EMNLP-CoNLL '12 Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning
A discriminative model for query spelling correction with latent structural SVM
EMNLP-CoNLL '12 Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning
Fast multi-task learning for query spelling correction
Proceedings of the 21st ACM international conference on Information and knowledge management
Flexible and efficient string similarity search with alignment-space transform
Proceedings of the 7th International Conference on Ubiquitous Information Management and Communication
Normalised LCS-based method for indexing multidimensional data cube
International Journal of Intelligent Information and Database Systems
User model-based metrics for offline query suggestion evaluation
Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval
A non-learning approach to spelling correction in web queries
Proceedings of the 22nd international conference on World Wide Web companion
Space-efficient data structures for Top-k completion
Proceedings of the 22nd international conference on World Wide Web
Mining search and browse logs for web search: A Survey
ACM Transactions on Intelligent Systems and Technology (TIST) - Survey papers, special sections on the semantic adaptive social web, intelligent systems for health informatics, regular papers
Efficient error-tolerant query autocompletion
Proceedings of the VLDB Endowment
Recent and robust query auto-completion
Proceedings of the 23rd international conference on World wide web
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In this paper, we study the problem of online spelling correction for query completions. Misspelling is a common phenomenon among search engines queries. In order to help users effectively express their information needs, mechanisms for automatically correcting misspelled queries are required. Online spelling correction aims to provide spell corrected completion suggestions as a query is incrementally entered. As latency is crucial to the utility of the suggestions, such an algorithm needs to be not only accurate, but also efficient. To tackle this problem, we propose and study a generative model for input queries, based on a noisy channel transformation of the intended queries. Utilizing spelling correction pairs, we train a Markov n-gram transformation model that captures user spelling behavior in an unsupervised fashion. To find the top spell-corrected completion suggestions in real-time, we adapt the A* search algorithm with various pruning heuristics to dynamically expand the search space efficiently. Evaluation of the proposed methods demonstrates a substantial increase in the effectiveness of online spelling correction over existing techniques.